State of Art on auto-scaling in Cloud Computing

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1 State of Art, February, 3rd 2013 State of Art on auto-scaling in Cloud Computing Victor Pinsembert, Julien Bideau, Malika Bahmad, Baptiste Chrétien, Sebastien Tanguy 1 Abstract Recently, Cloud Computing has known some new concepts that changed the way we use and design applications. Usually, Cloud systems use virtualization to allocate IT resources on demand. This on-demand allocation is called scaling. Scalability is a critical issue to the success of enterprises involved in the cloud business. The infrastructure need to automatically provide IT resources in case of a workload peak. In this paper, we will present a cloud computing auto scaling survey. We describe the key concepts and the architectural principles. Then we will present our state-of-the-art about the auto scaling problematic in cloud computing based on fifteen different papers. The purpose of this paper is to provide a better understanding of elasticity in cloud computing and also identify the main research directions in this area. Keywords Cloud Computing Autoscaling Elasticity SLA 1 Engineer Students, ESAIP, Angers, France Contents Introduction 1 1 Papers Casing Categories Explanation Papers Matrix Papers resume Guaranties Auto-Scaling Model for Cloud Computing System Service Level Agreement in Cloud Computing Conceptual SLA Framework for Cloud Computing Towards QoS-Oriented SLA Guarantees for Online Cloud Services 2.2 Cost Cloud Auto-scaling with Deadline and Budget Constraints Auto- Scaling to Minimize Cost and Meet Application Deadlines in Cloud Workflows Dynamic Scaling of Web Applications in a Virtualized Cloud Computing Environment Model-driven autoscaling of green cloud computing infrastructure 2.3 Performances Scaling in Cloud Environments Efficient Auto scaling in the Cloud using Predictive Models for Workload Forecasting A Performance Study on the VM Startup Time in the Cloud An Autonomic Auto-scaling Controller for Cloud Based Applications 2.4 Trade-Off Optimal Cloud Resource Auto-Scaling for Web Applications Quality-assured cloud bandwidth auto-scaling for video-on-demand applications Impact of CPU Utilization Thresholds and Scaling Size on autoscaling Cloud Resources References 9 Introduction For our final year in the engineering school ESAIP, we worked on scientific project in order to help a Computer Sciences Phd Student in the Cloud Computing area named Simon Dupont. In order to do so, we made a state of the art composed of fifteen scientific papers and classify them in a table. We hope it will provide a better understanding of the elasticity problematic in cloud computing, more precisely the auto scaling issue. It will also give the main research directions in this specific field. Thus, we identified several criteria to classify the papers. 1. Papers Casing 1.1 Categories Explanation Supplier/Client: All our papers talk about auto-scaling but with different point of view. Auto-scaling s goal is about how to best manage the resources in the cloud according to the fluctuating demand. So if we look at it from a different angle: it is an area ruled by offer and demand. Thus, we can distinguish two roles: the provider s or the customer s. Guaranties: Some papers are guaranty oriented, they focus on ways to improve the quality of auto-scaling. In other words, they talk about the commitments implied by guaranty contracts and what cost they occur when these contracts are not respected. Those papers usually talk about SLA and SLO. Cost: Other papers are cost oriented, they focus on the economic aspect of auto-scaling and the financial benefits it will bring to either party involved. Furthermore, this category can be coupled with the guaranty category because the non-respect of certain agreements and guaranties can lead to the payment of a fine. Performance: Papers about auto-scaling often focus on performance. It can be about the quality of service (QoS) or the response time of a request or a VM. Trade-Off: Some papers talk about the improvement of a QoS attribute despite a loss in a resource utilization attribute. It is typically a compromise between the QoS and resources utilization. Papers about trade-off usually describe methods to find the right balance between those attributes and how to achieve an overall minimal performance loss.

2 State of Art on auto-scaling in Cloud Computing 2/ Papers Matrix Categories Guaranties Cost Perf. Trade Off SaaS Provider [1] [2], [3] [4], [5] [6], [7] IaaS Provider [8], [1], [9] [4], [10] [11], [7] SaaS Customer [12] [13] [5] IaaS Customer [12], [9] [14] [15] 2.1 Guaranties 2. Papers resume Auto-Scaling Model for Cloud Computing System [8] In this paper, authors are talking about the Cloud computing auto scaling. They also present their work: An autoscaling algorithm which automatically provides virtual machine for the Cloud. The goal of this paper is to show that with Auto-scaling, Cloud computing systems can handling sudden load requirements and in the same time maintaining high resource utilization. In the first part (Architecture design), they explain how auto-scaling works by giving a short example of a Website which requires a quick response time and a high availability. In fact, web servers are often under-provisioned or overprovisioned and waste many resources. Indeed, when a server waste resources, it becomes less efficient. The more the webserver is efficient, the more you save energy and money in the same time. That s why cloud computing must be flexible and adaptable to the users demands. With the auto-provisioning system, you can expand or reduce the number of VMs according to the workload or the actives sessions on the server. A typical architecture includes three main components: Front-end load balancer Virtual cluster monitor system Auto-provisioning system with an auto-scaling algorithm Front-end load balancer can balance the requests between different virtual machines that perform the same application in a virtual cluster. Virtual cluster monitor system collects resource usages of all VMs for each virtual cluster that are running on Cloud. The energy cost can be reduced by remove the unused virtual machines. By this way, you can save energy and reduce costs of your system. In our opinion, for an efficient infrastructure, auto scaling is an obligation if you want to save money and have a better customer satisfaction rate Service Level Agreement in Cloud Computing [1] Nowadays, consumers are adopting Service-Oriented Architecture (SOA), quality and reliability of services become important aspects. However the requirements of consumer services vary greatly over time. That s why it is important to define a certain Quality of services (QoS). As traditional providers, Cloud solutions providers are committed through the Service Level Agreement (SLA), a document that contractually defines the quality of service expected. Metrics like QoS attributes are typically part of an SLA (such as response time and speed), but constantly changing. And to enforce the agreement, these parameters need to be monitored. This contract defines the parameters that will bring external quality guarantees, it may relate to a service operation or service. There is actually no standard for the specification and the establishment of SLAs. SLA described through the language WSLA (Web Service Level Agreement) has three main sections which are: Parties (customer and provider) SLA Parameters specified by metrics Obligations that define the guaranteed service level. Obligations are specified by one or more SLOs (Service Level Objectives). An SLO defines : Parties : The head of the security which is responsible for delivering what was promised (usually the supplier but the customer may have SLOs to respect) Service definition: The parameter related to the warranty. This parameter must be measurable. In the case of services, we are talking about quality attributes (eg response time, availability, throughput (nb request/secs),...) Obligations with the conditions and the actions guarantees: specific measurable characteristics of the SLA such as availability, throughput, frequency, response time, or quality. Authors present in the second part their cloud WSLA architecture. They described three main WSLA services for their architecture and their adaptations required in the cloud context: Measurement Services: These services are responsible for measuring the runtime parameters of cloud provider resources. Condition Evaluation Service: This service is responsible of getting the results from measurement services and evaluating the Service Level Objectives. Management Service: This service is responsible for taking corrective actions on violation of the Service Level Objectives. The authors concluded by explaining the context of cloud computing is not enough standardized. There is no universal set of metrics that can be monitored through cloud providers. There are attempts to standardize but at the end of the document, they suggest the use of basic metrics and establish best practices for measurements.

3 State of Art on auto-scaling in Cloud Computing 3/10 In our opinion, it s important to define the SLAs parameters and the way how they are measured, as accurate as possible. Until, there will be any standardization in cloud computing, you will need to use some tools like WSLA Framework to define your SLA. In the case you use auto scaling with a private cloud, you will need to define your own metrics in order to have the best SLA possible as possible. In case of a disrespect of a SLA, there will be penalties for the provider. It could be a disaster for a cloud provider which not respects the SLA with a huge cost and a bad customer satisfaction rate Conceptual SLA Framework for Cloud Computing [12] A service-level agreement (SLA) is a contract between a service provider and a customer. This document defines the quality of a service by giving a clear definition of the agreements. It specifies the levels of availability, performance, or other attributes of the service, such as billing. In order to define the SLA contract between both actors, the SLA should be easy to understand by the service consumer, it must contain the service performance level, Penalties if there are some violations and the business metrics. There are different requirements for each type of cloud service: Functional and non-functional ones. In this paper, only the non-functional requirements of services are treated, such as availability, scalability and response time. In the proposed framework, the SLA parameters are specified by metrics for four types of Cloud Computing services. These services are infrastructure as a service (IasS), platform as a service, software as a service, and storage as a service. The metrics define functions of how cloud service parameters can be measured and specify values of measurable parameters. At the end, the author presents three scenarios that can be applied for the cloud computing environment when consumers need to negotiate with cloud providers. The first one is a direct negotiation, it s the most common method that is used, the second one is negotiation via a trusted agent who have experience and the last scenario is negotiation with more than one agent, this scenario is efficient if cloud consumer requires more than one type of cloud services. As future work, SLA metrics will be designed and a simulation process will be implemented to test authors framework in the cloud computing environment. The result of this work will help consumers select the most reliable service Towards QoS-Oriented SLA Guarantees for Online Cloud Services [9] In the case of a cloud computing solution, the management of quality-of-service and SLA may give significant challenges to the performance, costs and dependability of online cloud services. When you get a purchase a cloud computing solution, you expect certain QoS like availability, response time for your company. That s why SaaS providers (like Amazon EC2) offer a service availability of at least 99.95%. The problem is they provide very few guarantees in terms of performance and dependability. That s why you will need to describe with great care the SLA terms between cloud provider and cloud customer, like service levels and the service level objectives (SLOs). Therefore, a Service Level Agreement (SLA) is a set of SLOs to meet. The SLA is negotiated between the cloud service provider and its customer with the contract. When you want to sign SLOs, you need to think on which QoS aspects you need. There are many QoS attributes you may consider such as performance, availability, reliability, cost, etc. For each aspect of QoS, multiple Indicators of quality of service may be considered. For instance, in order to implement availability metric, you can measure the abandon rate or the success rate of your solution. The cost metric is not widely used but could be both interesting for the customer or the provider: you can check the energetic cost of a service or the financial cost of using a cloud service. These cost metrics reflect the energy footprint of the solution. You define a SLO when the stakeholders are committed to provide a QoS metrics with a value higher/lower than a given threshold. In this paper, authors present a new cloud model based on service level agreement (SLA): SLAaaS (SLA-aware-Service). SLAaaS allows to a user to select the QoS aspects which he is interested in (e.g. performance, cost), and the QoS metrics apply to these aspects (e.g. service response time, financial cost). The customer can then choose the SLOs he/she wants to apply on the QoS metrics. In the second part, authors present a language use to describe cloud services associated to the QoS : The CSLA (Cloud Service Level Agreement) language. CSLA language is related to the WSLA and SLA@SOI but it integrates the QoS uncertainty and cloud fluctuations, such as confidence, penalty and fuzziness (defines the acceptable margin around the target value of SLO). The confidence level is the percentage of compliance of SLO clauses. We can also apply penalties in case of SLA violations, to compensate cloud service customers. Authors tried to give an equation to define violation penalties. Once a SLA is described with CSLA and established between a cloud service provider and a cloud customer you can use an online cloud controller system. This controller system is described in the third part of the paper. The aim of a controller cloud system is to propose efficient algorithms in order to control laws that calculate the best service configuration for the customer. It helps also to rapidly react to changes in cloud service usage, with auto-scaling for instance. At the end of the paper, they propose three case studies running on private clusters and Amazon EC2 public cloud to illustrate the language they introduce. Their experiments on online cloud services through the case studies successfully demonstrate the utility of SLAaaS. They also applied SLA with QoS metrics such as client request response time, availability, resource usage and resource cost. It s also possible to consider different metrics like service throughput or energetic

4 State of Art on auto-scaling in Cloud Computing 4/10 cost. In our opinion, in case of a subscribing to a cloud offer, you must define clearly SLOs with your cloud provider in order to have an efficient SLA. Indeed, for your company, you ll need the best response time and availability. For example, if you want to subscribe to Amazon, you will need to be careful to the different conditions you may find in the contract. The SLAaaS model apply to the CSLA language may be interesting to implement if you want to define your SLOs with the corresponding metric. One other problem could be the size of the different SLAs. Indeed, in some cases (e.g. Facebook), there will be SLOs for each services and the SLA corresponding will really hard to understand. why the implementation has to include 4 components: performance monitor, history repository, auto-scaling decider and VM manager. An evaluation is done using both simulations and real scientific application, it aims to evaluate the mechanism especially the link between deadline and workload, cost and different types of VM instance. The cost comparison shows that choosing appropriate instance type can save betwenn 20% and 45% compared to fixed instance types. Long unexpected VM startup delay could not only affect the performance, but can also dominate the utilization rate, and therefore the cost, especially for short deadline cases. Workload and job processing time are also very important factors in our mechanism, because they directly affect the number and type of provisioned instances. 2.2 Cost Cloud Auto-scaling with Deadline and Budget Constraints [14] This paper presents a dynamic scaling mechanism for the Cloud, which could automatically scale up and scale down underlying cloud infrastructures to accommodate to a changing workload based on application level performance metrics and job deadline. Previous works do not consider various types of VMs or total running cost for example. The mechanism presented in the paper is the first auto-scaling mechanism which addresses both performance and budget constraints in cloud. The main characteristics identified by the authors are: Unlimited resources limited budget: a cloud autoscaling mechanism should explicitly consider user budget constraints when acquiring resources. Non-ignorable VM instance acquisition time: Ignoring pending instances may result in booting more instances than necessary, therefore waste money. And if the startup time delay can be well observed and predicted, application s administrator can acquire machines in advance and prepare early for future workload peaks. Full hour billing model: a reasonable policy is that whenever an instance is started, it is better to be shut down when approaching full hour operation Multiple instance Types: Users can start different types of instances (standard, high-cpu and high-memory) based on their applications and performance requirement. The purpose of the study is to find out how to enable cloud applications to finish all the submitted jobs before user specified deadline at minimal cost. In order to reach this goal, autoscaling mechanism needs to ensure that the computing power of all acquired VM instances is sufficient to handle the workload. The choice of the architecture is also important, that s Auto-Scaling to Minimize Cost and Meet Application Deadlines in Cloud Workflows [13] In their previous research, they explored the cloud autoscaling problem with budget and deadline constraints for application and with a uniform performance requirement. The results of their research showed that auto-scaling mechanism was effective in meeting deadlines but the cost-efficiency could be better. In this paper, their research are for a more general application model. The objective is to ensure that all deadlines are met to conserved properly money, time and resource and avoid wastes. The scenario studied is more complex than in their previous research one. In this case, there could be multiple job instances submitted to the application. The auto-scaling mechanism takes into account the progress of submitted jobs and the changing workload. The solution is to make the scheduling and scaling step by step: This study is based on a Cloud application model illustrated below. Figure 1. Auto-Scaling Application Model Job: Different jobs may consist of different tasks. For example, for an online shopping Web site, although both inventory query and order submission requests need to go through the customer authentication service, inventory queries only need to deal with the databases while order submission

5 State of Art on auto-scaling in Cloud Computing 5/10 requests need to further go through the payment confirmation service and the receipt printing service. PREPROCESSING Step 1 Task Bundling : Task bundling is the action to forces adjacent tasks to run on the same instance. This method reduce data transfers, tasks use the same instance so they use the temporary results stored locally. Step 2 Deadline Assignment : Firstly, the initial deadline assignment is calculated. If the job cannot be finished in time, algorithm will schedule some high-cost tasks on a faster machine and then recalculate. DYNAMIC SCALING CONSOLIDATION SCHEDULING Step 3 Scaling : The step 2 scheduled an execution interval (T1-T0) for each task, and we know its running time (tm). Load Vector (LV) is defined as a ratio: tm / (T1-T0). We need to guarantee that the number of existing machines is always greater than load vector. Step 4 Instance consolidation : Usually, the billing period of cloud providers is one hour. Example: Windows Azure: Euros (extra small) Euros (extra-large instance) per hours. If a task is finished before the end of the hour, another task can be run on the same instance. Step 5 Dynamic scheduling : It s the scheduling of the task with the earliest deadline whenever an instance is available. They call it Earliest Deadline First (EDF). The result shows that this approach can achieve cost saving ranging from 9.8% to 40.4% compared to others. In our opinions, this solution based on a monitor-control loop is very important for large online shopping web site where the problem of cost optimization is essential. The monitor-control loop can help handle imprecise input parameters and make fast responses to dynamic changes Dynamic Scaling of Web Applications in a Virtualized Cloud Computing Environment [2] This paper talks about the importance of scalability in a virtualized cloud computing environment. In fact, it is considered as one of the main factors for enterprises success. The first part of this document describes the cloud computing, using virtualization. Cloud computing delivers IT resources (services and storage) on-demands to users depending on their needs, without caring about the maintenance of the physical IT resources. Three typical types of Cloud Computing services are presented: Software as a Service (SaaS) - Cloud providers offers ready-to-use cloud applications for their consumers. It allows clients to use business software services for a monthly fee instead of having to design, develop and host it on their own. Platform as a Service (PaaS) - PaaS is a way to rent hardware, operating systems, storage and network capacity over the Internet. The service delivery model allows the customer to rent virtualized servers and associated services for running existing applications or developing and testing new ones. Infrastructure as a Service (IaaS) - This model of service delivers a complete computing infrastructure, which can be configured, scaled, utilized on demand of its user. By subscribing to an IaaS cloud, an organization can avoid the costs and difficulties of purchasing, housing and maintaining various hardware and software components of an infrastructure. Amazon Elastic Compute Cloud (EC2) is an example of typical Cloud infrastructure service. In a second part, the paper discusses the scalability of the Cloud and the use of virtualization technologies. Different scaling indicators are used In order to monitor and track the performance. Some scaling indicators at the web server are given such as: Number of concurrent users. Number of active connections. Throughput (number of requests per seconds) In the last part of the document, the author illustrates an architecture design of a scaling scenario for an on-line web application, and the dynamic scaling algorithm for scaling a web application deployed in virtual machine on a Cloud. This algorithm is based on number of login users to the web application. As a result, infrastructure and management costs are reduced Model-driven auto-scaling of green cloud computing infrastructure [3] With cloud computing, the power consumption of information systems can be reduced by using virtualization. One of the big stakes of news computing technologies and cloud computing is to reduce the power consumption. By 2011, the power consumption of computing data centers is expected to exceed 100,000,000,00 kilowatt hours (kw h) and generate over 40,568,000 tons of CO2 emissions. Auto-scaling consists in dynamically allocates/removing resources to application and then save energy (balance between under/over-provisioning), reduce the cost without impacting the quality of service. With auto-scaling, Virtual Machine instances can be prebooted to avoid possible delay in case of workload peaks. These instances are standing by in a queue, there are not use during this time and waste a part of power consumption. In this paper, authors present a technique for minimizing the number of idle VMs in an auto-scaling queue. An application on the Cloud can request component deployed on VM with different configurations: Hardware: CPU (small, large), RAM

6 State of Art on auto-scaling in Cloud Computing 6/10 Software: application server (Tomcat, JBoss), OS (Linux, Windows), etc. The consumption of each VM varies, for example a small Amazon EC2 VM instance running a Linux-based OS consumes 150Wand costs $ per hour, while a large VM instance with Windows consumes 630Wand costs $ 2.88 per hour. This paper presents a model-driven engineering (MDE) approach called Smart Cloud Optimization for Resource Configuration Handling (SCORCH). The aim is to compare application requirement, VMs standing by in the queue and to compare them. If the configuration on the VM totally match with the configuration needed by the application then the VM is chosen, there is no problem. If the queue does not have an exact match, it may have a running VM configuration that can be modified to meet the requested configuration more quickly than provisioning and booting a new VM. The matching between applications, SCORCH uses feature models and the configuration option of the VM. A feature can describe an OS, an application server, the version of this application server, the type of CPU, etc... Figure 2. Monthly power consumption At the end of the paper, they compare SCORCH with two other approaches for provisioning VMs (non-optimized queue and without auto-scaling). The experiment uses instance types associated with Amazon EC2, 54 VMs. While both techniques reduced cost by more than 35%, deriving an auto-scaling queue configuration with SCORCH yielded a 50% reduction of cost and power consumption compared to utilizing the static provisioning technique. In our opinions, improvements in cloud computing techniques like auto-scaling could be upgraded, particularly in reducing the power consumption. The increasing demand for Internet access and services will pushes providers to develop method for reducing power consumption and operating cost. 2.3 Performances Scaling in Cloud Environments [15] Nowadays, the IaaS and PaaS providers give usually informations about how they manage auto scaling in their infrastructure, the SaaS providers don t reveal these kinds of informations. They consider these informations useless for the users. In this paper, authors decided to not consider scaling on the SaaS infrastructure. In order to do their work, they decided to use only Amazon offers (EC2 and AWS). In this paper, authors wanted to show the benefits automatic of using auto-scaling in your infrastructure. To do so, they present the difference between all of the three kind of scaling in cloud environments. They begin with a presentation of a manual scaling, which is used in the traditional environments. The problem of this method is you need to manually add resources in case of load peak. By this way, if you have a load peak, you need to wait some time before somebody put manually new resources and finally meet the demand. So in this case, there will be a high latency and maybe some unavailability of an application at load peaks. That s why usually in traditional environment, you have more resources than needed (over-provisioning) or not enough resources at load peaks (under-provisioning). Another kind of scaling is semi-automatic scaling provided by Amazon EC2 in cloud environments. In this case, you don t have to put manually resources on the cloud so you can reduce the latency but you still need to check your demand to see if the infrastructure is well sized. All the monitoring is manual: the provisioning is done by request. By this way, you still have latency during load peak. The last kind of scaling is the full automatic scaling provided by Amazon AWS. With this kind of scaling, you have both provisioning and monitoring in the automatic way. In this case, you need to configure your infrastructure and then, all the scaling is doing automatically. You can remove all the latency on your infrastructure. At the end of this paper, authors explained what the different components on the amazon AWS offer are. Finally, they did tests on the infrastructure to confirm scaling is doing great in automatically mode when you have more workload. In their results, they showed that the scaling work great and respond correctly to the different workload. In our opinion, auto-scaling is really important in an infrastructure in order to have a better availability. For instance, it can help cloud providers to improve their QoS by improving their availability rate. The manually scaling is not as useful as the auto-scaling because it needs to be monitored Efficient Auto scaling in the Cloud using Predictive Models for Workload Forecasting [4] Efficiency of autoscaling in the Cloud Computing is an important but very complex subject. This paper discusses the challenges involved in auto scaling in the cloud and develops a model-predictive algorithm. Three challenges to elastic resource provisioning are developed, the first one is the

7 State of Art on auto-scaling in Cloud Computing 7/10 workload forecasting, which consists in predicting the future utilization of resources. It shows that it is important to be able to predict in advance the workload changes, this can be done thanks to historical data. The second challenge is about identifying Resource Requirement for Incoming. It explains how important it is to know precisely, how many resources are needed according to the predicted workload. The last challenge is the allocation of resources while optimizing various cost factors. The purpose is to obtain a perfect optimization of the resource usage. Then, the number of resources has to be constantly adapted with the workload changes. The second part of the document talks about different solutions for previous challenges, thanks to algorithms as well as customer behavior modeling graphs. Authors manage to identify the right number of resources needed, it avoids starting with a huge number of unused machines. So, they reduce unused machines when the workload is more than what is expected. Other algorithms are used with different cost parameters or look-ahead periods in order to predict workload changes and resources usage. The last part of the document shows how the resource quantity is chosen in a just-in-time manner. Thanks to the algorithm, the cost can be minimized. The website s visitor number is also used as a workload indicator A Performance Study on the VM Startup Time in the Cloud [10] This paper discusses the VM startup time in the Cloud Computing. It compares three popular cloud providers: Amazon EC2, Windows Azure and Rackspace. First, the study defined the measurements of the VM startup time; and after, analyzes results of different experiments based on factors like time, OS size, instance type, data center location, etc. The VM startup time is defined as the duration from the time of issuing VM acquisition requests to the time that the acquired instances can be logged in remotely. The first experiment is about time of the Day. It appears that the VM startup time is independent of time of the day but for Amazon EC2 and Windows Azure, Linux machines are 9 times faster than Windows machines because the image is smaller. image size increases. However, Windows Azure has slower performance compared to Amazon EC2 and Rackspace. Figure 4. Average data transfer rate The third experiment is about the VM Instance Type. It shows that VM startup time increases as the instance type increases. The fourth experiment explains that the data center location is totally independent of the VM startup time. The fifth experiment is about multiple instances requests. It appears that for Windows Azure the startup time linearly increases as the number of instance increases but for Amazon EC2, it has no impact. Another experiment studies the VM release time, and shows that there is no difference depending on different factors. This paper proved that some factors like the OS size or the number of instances requested can influence the VM startup time. This VM startup time is very important for the auto scaling Cloud Computing, that s why we can t neglect them. If the VM startup times are longer than workflow peaks, the system will start and stop VM all the time, but it will never be benefic An Autonomic Auto-scaling Controller for Cloud Based Applications [5] The paper presents an autonomic auto-scaling controller: Figure 5. Autonomic auto-scaling controller scheme Figure 3. Average VM startup time The second experiment is about the OS Image Size. For all providers, the VM startup time increases linearly as the Smoothing consists in computing an estimate of the workload to be used for auto-scaling decisions. Instead of making allocation decisions based on short duration

8 State of Art on auto-scaling in Cloud Computing 8/10 spikes the controller needs to identify workload variations that will persist for long enough periods of time in order to launch or stop VMs. In order to do this, it s important to identify recurrent workload types, and find an adapted way to manage resources. Controller determines the optimal number of instances required. Overprovisioning increases the cost, so the controller s job is to find the minimum number of VMs needed to run the application. Resource allocator instructs the management infrastructure to launch or stop instances as required. The system adopts a lazy termination policy that only stops a VM, if it has been running just below a multiple number of hours (to minimize costs), and the controller is requesting a lower number of VMs. The controller incorporates a fast response smoothing filter, a numerical optimization technique for finely tune the controller parameters, and implements a lazy termination policy. In the system, there are one parameter for the smoothing filter, four parameters for the controller and one extra parameter for the cooling period. This extra parameter avoids a ping pong effect (frequent creation/termination of instances when the workload is extremely variable). Performance tests shown that the improved auto-scaling tracks more closely the peaks and the valleys and reacts faster to changes. In these different tests, the average cost reduction was 6.3%. 2.4 Trade-Off Optimal Cloud Resource Auto-Scaling for Web Applications [11] Cloud resources Auto-Scaling is all about prediction and responsiveness, we have to be able to know as precisely as possible when the client s resources will be inadequate for the impending client requests. By doing so, we can then adjust the number of VMs (Virtual Machines) to match the demand. We can do this in two different ways: Horizontal scaling: allocate (scaling out) or release (scaling in) IT resources of the same type. For example, we have one VM which struggle with the impending workload so we allocate another server to meet the demand. This is the most common way of scaling and also the cheapest. Vertical scaling: replace the current IT resource by another one with higher capacity (scaling up) or with lower capacity (scaling down). This type of scaling is less common and more expensive. It is also slower than horizontal scaling because of the downtime required during the replacement of the resource. Over more, starting a VM takes time, sometimes several minutes so the timing is crucial. We need to know the intensity of the impending workload minutes before the actual request fluctuation happens (burst or fall). In this paper, they invented a hybrid technic using predictive statistics to foresee both network and VM performances fluctuations. They record and monitor the demand continuously and store the results in a database for later analysis. By doing so, they can already determine patterns of traffic behavior depending on specific times (rush hours, holidays, week-ends, etc.). Then, every hour, they run predictive statistical analysis to determine the optimal allocation of VM for the incoming traffic at that time and act accordingly (scale up, scale down or neither). Their choice of doing the VM re-allocation each hour is not hazardous, they took into account the fact that most of public clouds charge their VMS hourly. In our opinion, this solution not only allows to the provider to run his VMs efficiently but it is also a very cost-effective solution. Indeed, nowadays, almost every cloud actor offers a pay-per-use payment system which is charged hourly Quality-assured cloud bandwidth auto-scaling for video-on-demand applications [6] Nowadays, Video-on-demand (VoD) providers are relying on data centers and Cloud Services to meet their needs in term of bandwidth. Indeed, they often encounter high peaks of demand and struggle to provide them with the adequate bandwidth asked. So in order to help resolve this kind of problem, the team propose a predictive resource auto-scaling system that dynamically books the minimum bandwidth resources from multiple data centers, thus allowing the VoD providers to match their short-term demand. The authors specifies that you can t find any contract based on bandwidth guarantee in Cloud providers nowadays. Thus, to conduct their research, the team considered that such an offer was indeed available on the market for clients with specific needs of bandwidth such as VoD providers. Their solution gather information from the history of bandwidth demands in each video channel using cloud monitoring services as well as time-series techniques. Then it estimate the amount of demands expected and decides the minimum bandwidth required to best satisfy the demand with a high probability (trying to reach a 100 Over more, VoD providers usually serve geographically distributed clients. To do so, they have to possess a collection of channels served by multiple data centers, probably owned by different Cloud providers. But in such a scheme, achieving the highest bandwidth utilization would mean having a video replicated to every data center which will greatly increase storage expenses. On the other hand, no video replication would reduce bandwidth utilization (some data centers containing very well appreciated video will be under-provisioned while others containing less rated videos will be over-provisioned).

9 State of Art on auto-scaling in Cloud Computing 9/10 In this purpose, the research team managed to find the right balance by exploiting the predictable anti-correlation between demands to optimized resource utilization. In my opinion, this research team offers more than a VoD oriented auto-scaling solution. They also point out the weak points of having multiple data centers and Cloud providers and propose a fair auto-scaling model to overcome those difficulties Impact of CPU Utilization Thresholds and Scaling Size on autoscaling Cloud Resources [7] Currently, cloud computing is one of the fastest growing segments of IT. Cloud computing mechanisms and methods like auto scaling are used to assure SLO (Service Level Objectives). Some of the most important factors in auto scaling are the setting of CPU thresholds and scaling size. Utilization threshold: According to the amount of the necessary resources, setting threshold permit to control the triggering of the auto scaling policies and then decrease/increase the number of running instances committed. Scaling size: During a provisioning process (after spikes or DDoS attacks for example), more instances (2, 4, 8, etc... ) can be added to deal with spikes of the incoming load. In this paper, the objective of the study is to find the optimal configuration of these factors that lead to minimizing the resource utilization and response time according to the SLO defined. To optimize the CPU utilization threshold value, they increased the threshold ranging from 50% to 90%. It is obvious that at 90%, the using of instances is higher than at 50% but the response time is lower. Then they determine (based on equations) the optimal % value in the case where the usage of instances and the response time are minimum and they found 80%. The authors concluded by explaining that these metrics (CPU Utilization Thresholds and Scaling Size) have a considerable impact on the performance and cost of your service. The optimal setting should lead to minimizing the cost in terms of the number of allocated instances and to provide an acceptable SLO for the cloud services. In our opinion, the most important is to analyze (by simulation) each case study you have and then configure the optimal setting for this case study. It will be different according to services you need, resources you have and which is the most important metrics. References [1] Pankesh Patel, Ajith Ranabahu, and Amit Sheth. Service level agreement in cloud computing [2] Trieu C Chieu, Ajay Mohindra, Alexei A Karve, and Alla Segal. Dynamic scaling of web applications in a virtualized cloud computing environment. In e-business Engineering, ICEBE 09. IEEE International Conference on, pages IEEE, [3] Brian Dougherty, Jules White, and Douglas C Schmidt. Model-driven auto-scaling of green cloud computing infrastructure. Future Generation Computer Systems, 28(2): , [4] Nilabja Roy, Abhishek Dubey, and Aniruddha Gokhale. Efficient autoscaling in the cloud using predictive models for workload forecasting. In Cloud Computing (CLOUD), 2011 IEEE International Conference on, pages IEEE, [5] Jorge M Londoño-Peláez and Carlos A Florez-Samur. An autonomic auto-scaling controller for cloud based applications. International Journal of Advanced Computer Science & Applications, 4(9), [6] Di Niu, Hong Xu, Baochun Li, and Shuqiao Zhao. Quality-assured cloud bandwidth auto-scaling for videoon-demand applications. In INFOCOM, 2012 Proceedings IEEE, pages IEEE, [7] F Al-Haidari, M Sqalli, and K Salah. Impact of cpu utilization thresholds and scaling size on autoscaling cloud resources. [8] Kuan-Ching Li Che-Lun Hung, Yu-Chen Hu. Autoscaling model for cloud computing. IEEE, [9] Toward qos-oriented sla guarantees for online cloud services. [10] Ming Mao and Marty Humphrey. A performance study on the vm startup time in the cloud. In Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on, pages IEEE, [11] Jing Jiang, Jie Lu, Guangquan Zhang, and Guodong Long. Optimal cloud resource auto-scaling for web applications. In Cluster, Cloud and Grid Computing (CCGrid), th IEEE/ACM International Symposium on, pages IEEE, [12] Mohammed Alhamad, Tharam Dillon, and Elizabeth Chang. Conceptual sla framework for cloud computing. In Digital Ecosystems and Technologies (DEST), th IEEE International Conference on, pages IEEE, [13] Ming Mao and Marty Humphrey. Auto-scaling to minimize cost and meet application deadlines in cloud workflows. In High Performance Computing, Networking, Storage and Analysis (SC), 2011 International Conference for, pages IEEE, [14] Ming Mao, Jie Li, and Marty Humphrey. Cloud autoscaling with deadline and budget constraints. In Grid Computing (GRID), th IEEE/ACM International Conference on, pages IEEE, 2010.

10 [15] Dominique Bellenger, Jens Bertram, Andy Budina, Arne Koschel, Benjamin Pfänder, Carsten Serowy, Irina Astrova, Stella Gatziu Grivas, and Marc Schaaf. Scaling in cloud environments. Recent Researches in Computer Science, State of Art on auto-scaling in Cloud Computing 10/10